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Free AIP-210 Exam Braindumps (page: 4)

Page 3 of 23
PDF with 90 Questions

Which of the following options is a correct approach for scheduling model retraining in a weather prediction application?

  1. As new resources become available
  2. Once a month
  3. When the input format changes
  4. When the input volume changes

Answer(s): C

Explanation:

The input format is the way that the data is structured, organized, and presented to the model. For example, the input format could be a CSV file, an image file, or a JSON object. The input format can affect how the model interprets and processes the data, and therefore how it makes predictions.
When the input format changes, it may require retraining the model to adapt to the new format and ensure its accuracy and reliability. For example, if the weather prediction application switches from using numerical values to categorical values for some features, such as wind direction or cloud cover, it may need to retrain the model to handle these changes .



Which of the following tools would you use to create a natural language processing application?

  1. AWS DeepRacer
  2. Azure Search
  3. DeepDream
  4. NLTK

Answer(s): D

Explanation:

NLTK (Natural Language Toolkit) is a Python library that provides a set of tools and resources for natural language processing (NLP). NLP is a branch of AI that deals with analyzing, understanding, and generating natural language texts or speech. NLTK offers modules for various NLP tasks, such as tokenization, stemming, lemmatization, parsing, tagging, chunking, sentiment analysis, named entity recognition, machine translation, text summarization, and more .



A classifier has been implemented to predict whether or not someone has a specific type of disease. Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

  1. Mean squared error
  2. Precision and accuracy
  3. Precision and recall
  4. Recall and explained variance

Answer(s): C

Explanation:

Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).



Which of the following describes a typical use case of video tracking?

  1. Augmented dreaming
  2. Medical diagnosis
  3. Traffic monitoring
  4. Video composition

Answer(s): C

Explanation:

Video tracking is a technique that involves detecting and following moving objects in a video sequence. Video tracking can be used for various applications, such as surveillance, security, sports analysis, and human-computer interaction. One typical use case of video tracking is traffic monitoring, where video tracking can help measure traffic flow, detect congestion, identify violations, and optimize traffic signals.






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